Forward-Deployed Architect, Credit Analytics & ML Platforms

Neurons Lab LTD.; Spain; On-site; EUR 120,000 - 180,000; Full time

Job summary

Neurons Lab LTD. in Spain is seeking a hands-on Forward Deployed Engineering Architect to lead the design and delivery of a production-grade credit analytics platform for regulated financial services.

You will own architecture, guide ML workflows from data to deployment, and collaborate with business and technology teams to ship robust, auditable solutions. Experience with Python/SQL, MLOps, Snowflake, and cloud platforms is essential.

Qualifications

• Track record of delivering production data or ML systems with ownership beyond prototypes and diagrams.

Responsibilities

• Translate business problems into an architecture, delivery plan and acceptance criteria, identifying data gaps before they become blockers.

Skills

Python

SQL

MLOps

Leadership

English communication

Tools

Snowflake

Azure ML

Streamlit

SQL

Python

Job description

About The Role

We are looking for a hands-on Forward Deployed Engineering (FDE) Architect to help develop a credit analytics platform for a regulated financial-services business operating in the UK and Ireland. The platform brings together lending and servicing data to support portfolio analysis, credit-risk modelling and better business decisions. Current work covers probability of default, redemption and prepayment, expected credit loss, and interactive analytics. The next challenge is to turn this work into a reliable, maintainable platform that can support different portfolios and client needs. You will work directly with the client’s business and technology teams, alongside a credit-domain and modelling lead and other engineers. You will own the engineering architecture, contribute production code, and help the team decide what to build and how to deliver it. We need someone who can question assumptions, propose simpler alternatives and explain technical trade-offs clearly.

What you will do

• Translate business problems into an architecture, practical delivery plan and clear acceptance criteria. Identify gaps in requirements, data and dependencies before they become delivery problems.
• Design the path from working models and dashboards to a production-ready ML platform. Make deliberate choices about shared components, portfolio-specific configuration, model calibration and future client onboarding.
• Build and improve reproducible workflows for data preparation, feature engineering, training, validation, scoring and deployment. Establish clear versioning for code, data, models and calibration logic.
• Work with the modelling lead on how probability of default, prepayment, lifetime risk and loss estimates are calculated, tested and exposed to applications. Keep business definitions consistent across models, tables and dashboards.
• Strengthen the data foundation: trace results to source, investigate data-quality issues, document assumptions and build automated checks around important transformations.
• Improve the reliability of the Snowflake-backed analytics application, including deployment, testing, permissions and integration with model outputs.
• Put appropriate MLOps practices in place, including repeatable releases, monitoring, failure handling, rollback and operational documentation.
• Design for privacy, access control, auditability and model governance. Work with domain experts to make validation, stability and fairness checks practical and reviewable.
• Lead technical discussions, review code, unblock engineers and keep the client informed about progress, risks and architectural decisions.
• Assess where natural-language or LLM-assisted analytics would add value, without losing focus on trustworthy data, robust models and useful products.

What we are looking for

• A track record of designing and delivering production data or ML systems, with ownership extending beyond prototypes and architecture diagrams.
• Strong hands-on Python and SQL. You should be comfortable investigating a dataset, reviewing modelling code and implementing critical parts of the solution yourself.
• Practical MLOps experience: reproducible pipelines, model and data versioning, testing, deployment, monitoring and the operational consequences of model updates.
• Experience with cloud data platforms and modern warehouses. Snowflake experience is particularly relevant; you should be able to reason about data access, performance, cost and deployment constraints.
• An understanding of model evaluation, calibration, data leakage, changing populations and the difference between model quality and system reliability.
• The ability to work through incomplete documentation and inconsistent business definitions, and turn findings into maintainable code and clear documentation.
• Strong client-facing communication in English. You can explain trade-offs to non-technical stakeholders, constructively challenge a requirement and take responsibility for the resulting decision.
• Experience leading a small technical team while remaining actively involved in implementation.

Useful Additional Experience

• Credit-risk, lending or servicing analytics, including PD, LGD, ECL, prepayment or portfolio stress testing.
• Regulated financial-services delivery and model governance.
• Streamlit, Azure ML, semantic data layers or analytics applications built on Snowflake.
• Configurable ML products serving multiple portfolios or clients.
• Evaluated natural-language analytics, text-to-SQL or LLM-assisted workflows.

Current technical environment

Snowflake, SQL, Python, Streamlit and an Azure ML environment. The team also uses AI-assisted development tools. You will help evolve the architecture based on the product’s needs rather than introduce a predetermined stack.

What we would like to discuss

• A production ML or analytics system you personally helped build, including the architecture decisions and code you owned.
• How you would adapt a model and its calibration for a new portfolio while preserving reproducibility, access boundaries and validation.
• A time you challenged a business or technical assumption, proposed an alternative and delivered a better outcome.